Noise-free accurate count of microbial colonies by time-lapse shadow image analysis

Ogawa, Hiroyuki; Nasu, Senshi; Takeshige, Motomu; Funabashi, Hiroaki; Saito, Mikako; Matsuoka, Hiroaki

doi:10.1016/j.mimet.2012.09.028

Cited by 16 publications

(15 citation statements)

References 20 publications

(23 reference statements)

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“…Bae et al (2009) proposed a microbiological instrument integrating a colony locator, a forward scatterometer and a 2D motion controller that not only counts and locates the bacterial colonies but also automatically measures the forward-scattering signature to identify the species of the bacterial colony under investigation. Ogawa et al (2012) proposed time-lapse shadow image analysis (TSIA), which involves capturing one image of the Petri dish each hour and then analyzing the differences according to shadows. This technique enables observers to identify the locations of colonies.…”

Section: Introductionmentioning

confidence: 99%

Automated counting of bacterial colonies by image analysis

Chiang

Tseng

Zhong

et al. 2015

Journal of Microbiological Methods

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Section: Introductionmentioning

confidence: 99%

Automated counting of bacterial colonies by image analysis

Chiang

Tseng

Zhong

et al. 2015

Journal of Microbiological Methods

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“…Several CDIA based computer colony counters (CCC) are presently available on the market (reviewed in [12]) including CCC specially designed for counting mammalian cell colonies (GelCount™, Oxford Optronix Ltd., UK, http://www.oxford-optronix.com/). Also, a number of "homemade" CDIA based systems for automated colony counting have been reported from several laboratories [13]- [18].…”

Section: Colony Countingmentioning

confidence: 99%

Image Analysis in Microbiology: A Review

Pushchino¹

2016

JCC

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This review is focused on using computer image analysis as a means of objective and quantitative characterizing optical images of the macroscopic (e.g. microbial colonies) and the microscopic (e.g. single cell) objects in the microbiological research. This is the way of making many visual inspection assays more objective and less time and labor consuming. Also, it can provide new visually inaccessible information on relation between some optical parameters and various biological features of the microbial cultures. Of special interest is application of image analysis in fluorescence microscopy as it opens new ways of using fluorescence based methodology for single microbial cell studies. Examples of using image analysis in the studies of both the macroscopic and the microscopic microbiological objects obtained by various imaging techniques are presented and discussed.

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“…Up to now, the majority of HW/SW imaging solutions related to bacteria colony growth have been focused on providing automated colony counting features, especially for application fields relying on exact (quantitative) and fast bacterial load estimation, such as food and beverage safety [2], [3], [4] or various environmental control and specific clinical usages [5], [6], [7]. Clinical bacteriology usually requires qualitative bacterial load estimation even in cases where massively confluent colonies growth occurs and traditional bacterial enumeration systems would fail.…”

Section: Related Workmentioning

confidence: 99%

Multistage classification for bacterial colonies recognition on solid agar images

Ferrari

Signoroni

2014

2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings

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Abstract-The advent of laboratory automation in clinical microbiology is entailing a revolution in the way most common bacteriological clinical exams are accomplished. As an essential part of these systems, digital recording and processing of cultured bacteria images is expected to improve plate reading, with a key role of image analysis tools in guaranteeing cost-effectiveness, accuracy, flexibility and reliability of the clinical tasks. In this work, we propose an image analysis system capable to address the complex problem of different bacteria species identification on cultured agar plates. Our solution is based on a modular segmentation/classification pipeline where a chain of supervised classification stages provides solutions to a series of nested task issues, from foreground separation toward isolated colony detection and classification. Performance assessment, based on an experimental dataset obtained in standardized laboratory conditions, clearly demonstrates the feasibility and the potentiality of the proposed solution and favorably opens to generalizations as well as to clinical validation studies.

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Noise-free accurate count of microbial colonies by time-lapse shadow image analysis

Cited by 16 publications

References 20 publications

Automated counting of bacterial colonies by image analysis

Automated counting of bacterial colonies by image analysis

Image Analysis in Microbiology: A Review

Multistage classification for bacterial colonies recognition on solid agar images

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